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基于功能连接信息熵的精神分裂症EEG分类研究

Research on EEG Classification of Schizophrenia Based on Information Entropy of Functional Connection
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摘要 为了提高精神分裂症的有效诊断,利用网络功能连接信息熵的方法对51例精神分裂症患者和56例年龄匹配的正常人的脑电信号(Electroencephalogram,EEG)进行了分类。通过采用分频技术、相位同步分析方法、信息熵方法、支持向量机(Support Vector Machine,SVM)分类方法,大幅提高了分类准确率(98.13%),实现了对精神分裂症的有效诊断。该分类方法主要涉及两阶段:利用分频技术和相位同步分析方法,获得各频段的脑电信号在各个时间点的功能连接矩阵;基于整个时间域上的功能连接计算各频段的信息熵,并将其分别作为功能脑网络的分类特征训练SVM分类器,进而对两组被试分类。分类结果表明,该方法大幅提高了精神分裂症检测的准确率。 In order to improve the effective diagnosis of schizophrenia, this paper uses the method of network functional connection information entropy to classify the Electroencephalogram(EEG)of 51 patients with schizophrenia and 56 agematched normal subjects. This paper achieves an effective diagnosis of schizophrenia by using the methods of frequency division, phase synchronization analysis, information entropy and Support Vector Machine(SVM), and greatly improves the classification accuracy. The classification method mainly involves two stages. First, the frequency division technique and the phase synchronization analysis method are used to obtain the functional connection matrix of the EEG signals in each frequency band at each time point. Second, the information entropy of each frequency band is calculated based on the functional connections over the entire time domain. And, the information entropy of functional connectivity is used as the classification feature of the functional brain network to train the SVM classifier, then the two groups of subjects are classified. The classification results show that the method proposed in this paper greatly improves the detection accuracy of schizophrenia.
作者 李佩珍 王彬 牛焱 田程 相洁 LI Peizhen;WANG Bin;NIU Yan;TIAN Cheng;XIANG Jie(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)
出处 《计算机工程与应用》 CSCD 北大核心 2019年第22期239-244,共6页 Computer Engineering and Applications
基金 国家自然科学基金(No.61873178) 中国博士后科学基金(No.2016M601287)
关键词 精神分裂症 脑电信号(EEG) 相位同步 功能连接信息熵 SVM分类 功能脑网络 schizophrenia Electroencephalogram(EEG) phase synchronization information entropy of functional connection SVM classification functional brain network
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  • 1顾凡及,宋如垓,王炯炯,范思陆,阮炯.不同状态下脑电图复杂性探索[J].生物物理学报,1994,10(3):439-445. 被引量:16
  • 2王兴元,骆超,谭贵霖.EEG动力学模型中混沌现象的研究[J].生物物理学报,2005,21(4):307-316. 被引量:8
  • 3侯澍旻,李友荣,刘光临.一种基于KS检验的时间序列非线性检验方法[J].电子与信息学报,2007,29(4):808-810. 被引量:29
  • 4杨福生 高上凯.生物医学信号处理[M].高等教育出版社,1995.318-369.
  • 5吴善元 王兆军.非参数统计方法[M].北京:高等教育出版社,1996..
  • 6Daniel KF. Analysis of LVQ in the context of spontaneous EEG signal classification. Inpartial fulfillment of the requirements for the degree of master of science Colorado State university, Fort Collins, Colorado, Summer, 1996
  • 7Anderson CW, Zlatko Sijercic. Classification of EEG Signals from four Subjects During Five Mental Tasks. Solving Engineering Problems with Neural Networks: Proceedings of the Conference on Engineering Applications in Neural Networks. Turku, Finland: Syste
  • 8Anderson CW, Stolz EA, and Shamsunder. Discriminating Mental Tasks Using EEG Represented by AR Models. Montreal,Canada:Proceedings of the 1995 IEEE Engineering in Medicine and Biology Annual Conference, 1995,20-23
  • 9Klimesch WG. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews, 1999, (29):169-195
  • 10Gert Pfurtscheller, Neuper C. Motor imagery and direct brain- computer communication. Proceedings of the IEEE,2001,89(7):1123-1134

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